3D lane detection system based on stereovision

Nedevschi, Sergiu; Schmidt, Rolf; Graf, T.; Danescu, Radu; Frentiu, Dan; Mariţa, Tiberiu; Oniga, Florin; Pocol, Ciprian

doi:10.1109/itsc.2004.1398890

Cited by 110 publications

(55 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thepitch angle is the one that varies more significantly, following the movement of the vertical component of the vanishing point, v Zt 2-The yaw angle is steady, since although it depends on the variation of the pitch angle, its influence is highly attenuated by the arctangent expression shown in Eq. (3).…”

Section: Pinhole Camera Projectionmentioning

confidence: 99%

See 1 more Smart Citation

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto

Laborda

Salgado

2010

Machine Vision and Applications

View full text Add to dashboard Cite

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance-cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

show abstract

Section: Pinhole Camera Projectionmentioning

confidence: 99%

“…For instance, several works address sub-pixel accuracy lane markings models [3][4][5] using calibration information. Others use image alignment between the stereo pair to detect volumetric objects on the road plane [6,7], or to enhance lane marking detection [8].…”

Section: Introductionmentioning

confidence: 99%

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto

Laborda

Salgado

2010

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…Roads are not always straight. Thus, parabolic curves [22], clothoids [23], B-splines [24], or active contours (snakes) [25] are more complex models adapted to curved road. These parametric models improve noisy bottom-up detection due to their width and curvature constraints, but urban environments are more difficult to model given the presence of parked cars that do not fit some of the width restrictions.…”

Section: Road Limitsmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

show abstract

“…And Table 2 lists the initial parameter values and its variation. Conventional approaches [14], [17] defines the initial parameters as experience value depending on camera's position and road information. It is possible to revise the variation value of parameters to ensure actual lane locate in restrain area according to matrix Eq.…”

Section: Pld Implementationmentioning

confidence: 99%

Predictive Lane Detection by Interaction with Digital Road Map

Wang

Chapuis

2012

Journal of Information Processing

View full text Add to dashboard Cite

This paper presents a robust hybrid approach to Predictive Lane Detection -PLD, which utilizes information from digital map to improve efficiency and accuracy to vision-based lane detector. Traditional approaches are mostly designed for well maintained and simple road conditions like motorway or interstate road with clear lane markers, to solve out the estimation problems of coming road shape as well as vehicle's position and ego-state, which however becomes ambiguous or unavailable in the complicated road environment and under difficult weather or illumination conditions. In this paper, the proposed approach refers to vehicle localization on digital map for road geometry estimation, which gives strong cues for vision-based detector to limit the search region of road candidates and suppress noise. In addition, other information from digital map like lane marker painting color and categories is utilized in the high level of data fusion on road geometry estimation. Real and synthesized road experiment results verified the effectiveness and efficiency of our approach.

show abstract

3D lane detection system based on stereovision

Cited by 110 publications

References 14 publications

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

A Hybrid Vision-Map Method for Urban Road Detection

Predictive Lane Detection by Interaction with Digital Road Map

Contact Info

Product

Resources

About